Predicting genetic interactions, cell line dependencies and drug sensitivities with variational graph auto-encoder

Abstract

Large scale cancer genomics data provide crucial information about the disease and reveal points of intervention. However, systematic data have been collected in specific cell lines and their collection is laborious and costly. Hence, there is a need to develop computational models that can predict such data for any genomic context of interest. Here we develop novel models that build on variational graph auto-encoders and can integrate diverse types of data to provide high quality predictions of genetic interactions, cell line dependencies and drug sensitivities, outperforming previous methods. Our models, data and implementation are available at: https://github.com/aijag/drugGraphNet.

Advancements in medical diagnosis and treatment through machine learning: A review

“…Xie et al (2017), examined the extraction of numerous features, data reduction, and data denoising usage of autoencoder models in genomics. An autoencoder that adopts a low-dimensional representation of high-dimensional data, such as genetic data, is called a variational autoencoder (VAE) (Gervits & Sharan, 2022). In genomics, VAEs have been utilized for data downsizing and dimension reduction.…”